plot_wordcloud

---

method plot_wordcloud(index=None, title=None, legend=False, figsize=(900, 600), filename=None, display=True, **kwargs)[source]

Plot a wordcloud from the corpus.

The text for the plot is extracted from the column named corpus. If there is no column with that name, an exception is raised.

Parameters

index: int, str, sequence or None, default=None Index names or positions of the documents in the corpus to include in the wordcloud. If None, it selects all documents in the dataset. title: str, dict or None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend: bool or dict, default=False Does nothing. Implemented for continuity of the API. figsize: tuple, default=(900, 600) Figure's size in pixels, format as (x, y). filename: str or None, default=None Name of the file. Use "auto" for automatic naming. If None, the figure is not saved. display: bool or None, default=True Whether to render the plot. If None, it returns the matplotlib figure. **kwargs Additional keyword arguments for the [Wordlcoud][wordlcoud] object.

Returns

go.Figure or None Plot object. Only returned if display=None.

See Also

plot_ngrams

Plot n-gram frequencies.

Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import fetch_20newsgroups

>>> X, y = fetch_20newsgroups(
...     return_X_y=True,
...     categories=[
...         'alt.atheism',
...         'sci.med',
...         'comp.windows.x',
...     ],
...     shuffle=True,
...     random_state=1,
... )
>>> X = np.array(X).reshape(-1, 1)

>>> atom = ATOMClassifier(X, y)
>>> atom.textclean()
>>> atom.textnormalize()
>>> atom.plot_wordcloud()